Robust Fingerprint Identification System Using Backpropagation and ART Neural Networks

This paper describes a robust minutiae-based fingerprint identification method suitable for use in small populations. System employs two serially connected neural networks in which fingerprint feature extraction is carried out by the first network – a backpropagation neural network and matching by the second - an adaptive resonance theory network which performs the decision making task of matching acquired fingerprint to templates in a database. The approach has been applied to a real database of noisy fingerprints derived from the 2002 Fingerprint Verification Competition (FVC2002) and has achieved error rates as low as 4% at penetration rates of 100%.